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5 March 2021 Advancing metal halide perovskite development with machine learning
Meghna Srivastava, John M. Howard, Tao Gong, Erica Lee, Qiong Wang, Antonio Abate, Marina S. Leite
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Proceedings Volume 11681, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices X; 116810J (2021) https://doi.org/10.1117/12.2576253
Event: SPIE OPTO, 2021, Online Only
Abstract
Low-cost, high-efficiency metal halide perovskite solar cells (PSC) are a promising alternative to Si photovoltaics, but poor stability currently precludes commercialization. We present a framework for accelerated PSC design using machine learning (ML) to identify optimal compositions, fabrication parameters, and device operating conditions. We present four examples showcasing our ML roadmap using various types of neural networks, applied to diverse problems such as forecasting time-series photoluminescence (PL) from perovskite thin films, projecting PSC power output and degradation over time, and predicting figures of merit from simple, high-throughput experimental procedures. Our paradigm informs the rational development of perovskite devices, providing an accelerated pathway to commercialization.
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Meghna Srivastava, John M. Howard, Tao Gong, Erica Lee, Qiong Wang, Antonio Abate, and Marina S. Leite "Advancing metal halide perovskite development with machine learning", Proc. SPIE 11681, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices X, 116810J (5 March 2021); https://doi.org/10.1117/12.2576253
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KEYWORDS
Perovskite
Machine learning
Metals
Data modeling
Humidity
Lead
Process modeling
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